One of the practical application of generative models trained on large datasets is their strong generalization capabilities for semantic image editing, often obtained via latent space interpolation [1]}, [2]}, [3]}. Applying these capabilities to an existing reference image was mostly achieved by GAN “inversion” techniques [4]}, and very recently by fine-tuning large diffusion models [5]}, [6]}, [7]}, [8]}, [9]}.
However, to the best of our knowledge, there are no existing large-scale models to-date which can manipulate an existing input reference video.
